Industrial Output Research Articles

Impact of trade on industry-level output

This study examines whether a country's trade significantly increases industry-level output and whether this impact is greater for industries with higher levels of differentiation. To empirically answer these questions, the Frankel and Romer [(1999). “Does Trade Cause Growth?” American Economic Review 89 (3): 379–399] method, which uses the geographical component of trade as an instrument, is extended in two ways. First, the study examines the impacts on industry-level output instead of country-level output. Second, an index of industry differentiation based on Rauch [(1999). Networks Versus Markets in International Trade.” Journal of International Economics 48 (1): 7–35] is introduced. Using a two-stage least squares estimation and data for 20 manufacturing industries and 99 countries in 2015, it is found that the impact of a country’s trade on industry-level output and output per worker is heterogeneous across industries. A one-percentage-point increase in the trade/GDP ratio results in a larger effect for industries with higher degrees of differentiation, ranging from 0.340% to 1.736% for industrial output and from 0.754% to 1.566% for industrial output per worker. This heterogeneity with respect to industry differentiation is robust even when controlling for latitude and institutional variables. These results suggest that a country’s trade affects industry-level output differently depending on the industry’s level of differentiation.

Investigating the Relationship Between the Dynamics of the Structure of Intermediate Consumption and Output of Industries: Cross-Country Analysis

This paper examines one of the main prerequisites of the inter-industry balance methodology on the proportional dependence of the output of industries and their intermediate consumption. The statistical base of the study is the world input-output tables collected for the period from 2000 to 2014. According to the results of the work, it was revealed that the prerequisite of a proportional change in the vector of intermediate consumption with a change in output is not consistent. However, significant positive relationships were found between intermediate consumption and the output of industries. On the basis of the identified relationships, we conducted a clustering procedure on industries, where, in particular, clusters with strong, medium, and weak relationships between intermediate consumption and output are distinguished. Thus, the paper proposes to expand the input-output methodology by introducing proportionality coefficients for changes in the intermediate consumption of an industry with a change in its output. Proportionality coefficients are proposed to be obtained on the basis of regression equations built on statistical data from the input-output tables. It should be especially noted that in addition to obtaining point forecasts of intermediate consumption for each industry when its output changes, it is also possible to obtain probability distributions for the gross output of each industry, their value added, wages, taxes, and profits through Monte Carlo simulations based on the constructed regression equations, which will facilitate a scenario analysis of the development of the considered economic system. The results of this work can be useful to executive authorities in planning large state economic projects as well as in assessing the impact of external shocks on the countryʼs economy.

The Role of Labor Force, Physical Capital, and Energy Consumption in Shaping Agricultural and Industrial Output in Pakistan

The labor force, physical capital, and energy sources are key economic drivers that enhance the output levels of developing economies. Despite their significance, the impact of these factors on agricultural and industrial output in Pakistan remains underexplored. This study aims to address this gap by examining the effects of the labor force, physical capital, and energy consumption—including electricity, oil, and gas—on agricultural and industrial output. Additionally, we explore the combined effects of electricity and oil consumption on these sectors. Based on unit root test results, which indicate that the variables as either I(0) or I(1), the Autoregressive Distributed Lag (ARDL) technique is selected. This method is particularly effective for handling mixed integration orders and provides robust estimates in small sample sizes, allowing for a thorough examination of both short- and long-run dynamics. Using annual time-series data from Pakistan for the period 1990–2022, the empirical results reveal that higher levels of the labor force, physical capital, electricity consumption, oil consumption, and gas consumption significantly increase agricultural and industrial output in both the short and long run. Furthermore, the findings reveal that the combined effect of electricity and oil consumption has a stronger impact on agricultural and industrial output compared to their individual effects. These results are further validated using alternative econometric techniques such as DOLS and FMOLS. To enhance Pakistan’s agricultural and industrial performance, policies should prioritize investment in human capital and physical capital development, ensure reliable and efficient energy supplies, and promote integrated energy management strategies. These steps are crucial for fostering sustainable economic growth and productivity in both sectors.

The coordinated development of higher vocational education, scientific and technological innovation, and sustainable economic development: Spatial distribution, influencing factors and future prediction

High-quality development in China requires higher vocational education, scientific and technological innovation, and sustainable economic development. The spatial distribution patterns of these factors show higher levels in the east and coastal areas compared to the west and inland regions, emphasizing the need for coupling coordination with the social economy. This study examines the impact of sustainable economic development on the coupling coordination degree using the spatial Durbin model. The results show a positive promotion and spillover effect, with regional variations. The main factors affecting the difference in coupling coordination are the amount of technology market contracts, fiscal expenditure on science and technology, patent application authorizations, tertiary industry output value, and the number of R&D institutions. According to the grey prediction model, the coupling coordination degree is expected to increase from 2022 to 2025, but achieving primary coordination may still be challenging in some areas. Therefore, strategies that utilize regional characteristics for coordinated development should be developed to improve the level of coupling coordination and create a mutually beneficial environment.

Highly efficient broadband solar thermal absorber for domestic renewable energy solutions

With the invention of the photonics techniques, a new structural design for thermal energy absorber is designed which uses the multilayer structure to absorb the solar energy and this solar energy is converted to thermal energy which is possible to be utilized in industrial heating applications. The use of solar energy makes it a sustainable option to meet the high growing industrial output energy. Materials like Titanium (Ti), Titanium Carbide (TiC), Silicon (Si) are used for this purpose. The use of these materials makes it more cost-friendly compared to the other similar options. The higher absorption rate is achieved by optimizing the design layers. The flowchart for the optimization in materials as well as design is also shown in this research. The optimization makes the design more compact and handy for industrial use. The advantage of these designs over the other designs is the choice of materials as well as their compact size. The results are also analyzed for the wide angle of incidence and polarization. All the results obtained show that the design is highly efficient in the solar spectral range and can be a renewable energy option for industrial and domestic heating applications.

Conformable fractional accumulation in triangular fuzzy sequences grey nonlinear model for tertiary industry gross output forecast

The prediction of the tertiary industry's output value is essential for enhancing the market economic system and providing guidance for the government in formulating relevant economic policies. The tertiary industry encompasses various sectors such as finance and transportation, and its total output value exhibits uncertain and nonlinear trends. Triangular fuzzy number sequences contain more comprehensive information than exact number sequences. Therefore, a nonlinear grey Bernoulli model with conformable fractional accumulation for triangular fuzzy sequences is proposed. Firstly, the conformable fractional accumulation generating operation is utilized to mitigate the volatility tendency of the original sequences. Additionally, a time power function term is introduced to capture the power exponential trend. Furthermore, an improved whale optimization algorithm incorporating opposition-based learning strategy and adaptive control parameters is employed to optimize the model parameters, which has been verified through algorithm comparison experiments. Three existing grey models are used as competing models to validate the prediction accuracy of our proposed model. Finally, we utilize this proposed model to predict the tertiary industry's output values in both Yangtze and Pearl River Deltas for 2024–2026.

Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry’s output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy.

Forward osmosis for concentrating lithium-enriched brine: From membrane performance to system design

Using concentrated salt-lake brine as the draw solution (DS), forward osmosis (FO) has been proposed as the low carbon footprint concentration technology for extracting water from lithium-enriched brine. From membrane modules to a process, there exist tremendous risks when considering total cost in time and investment for a trial. To address this issue, a bridge between the FO membrane/ module characteristics and a FO system design is highly desirable. This study for the first time developed a system design model using hollow fiber (HF) FO membrane modules for FO concentration of lithium-enriched brine. The concentration polarization along the module, the membrane structural parameters (S), crossflow velocity, and DS concentration were the main factors considered in the model. The effect of all factors on module water flux and total membrane area was systematically investigated for a typical industrial output of 10 k ton of Li2CO3. A master curve of the water flux verse feed concentration for the tailor-made HF modules was utilized as the basis. On a module scale, the structural parameter S is of first importance and indicates requirement of FO membranes with low S. The crossflow velocity has relatively small impact to the membrane area, but not negligible. On a system scale, the empirical equation of the structural parameters and crossflow velocity on the theoretical minimum system membrane area was summarized. The results show that increasing the stages can reduce the number of modules and required DS flow. Increasing DS dilution rate in system design efficiently utilizes the osmotic pressure of DS. The present model serves as a practical solution for process optimization and FO membrane selection. The methodology is valuable for other FO applications using HF FO membranes.

Estimation of the Impact of Government Spending on Industrial Sector Development: A Case Study of Iraq for the Period (1990-2020)

The research mainly attempted to find out the impact of the public expenditure on the development of the Iraqi industrial sectors according to the period (1990 to 2020), as well as several other control variables. The analysis of available data suggested that the indicators of industry sector and fiscal policy tools in Iraq underwent great changes not only because of economic and political instability experienced by the country throughout the study period but also the new-emerged situations that followed, like the cases of wars, economic sanctions, and changes in governance systems that contributed significantly in creating a halt in fiscal policy. Empirically, the study ascertained several outcomes from the analysis. In this case, the empirical test of the hypothesis showed that there was a negative correlation between current expenses and manufacturing output, significant at a level of confidence (10%). On the opposite side, the standard correlation between capital outlay and industrial output is of a positive sign (0,05). Moreover, a current expenditure with an industrial output would make a negative relationship and would become significant at the level of significance of 10%, whereas investment expenditure and industrial output making a positive relationship with a level of significance of 5%. As part of study, it recommended the re-organization of public expenditure structure, enhancing discipline of budget formulation. This, in turn, will support the development of budgetary programs for the economy, particularly, the industrial sector.

Drivers of industrial carbon emissions in the Yangtze River Delta region, China: A combination of decoupling and LMDI models

ABSTRACT The expansion of the industrial economy has resulted in high carbon emissions (CE) from the Yangtze River Delta (YRD) region. Industrial CE reduction is a non-negligible way to achieve green and low-carbon development. In this paper, we aim to study the decoupling effect of industrial CE and its socio-economic influencing factors in the YRD region from 2005 to 2020. First, the decoupling relationship between industrial CE and industrial output in the YRD region is measured. Then, using the LMDI method, the variation of industrial CE is decomposed into six factors: energy structure ( Δ c s ), energy intensity ( Δ c i ), economic scale ( Δ c e ), labor structure ( Δ c l ), urbanization rate ( Δ c u ) and population size ( Δ c p ) effect. Subsequently, by combining with the decoupling and LMDI models, the contribution of each factor to the decoupling of industrial CE is studied, and the elasticity of each factor is expressed by η s , η i , η e , η l , η u , and η p . The main findings show that: (1) There are two states of weak and strong decoupling between industrial CE and the industrial economy in the YRD. (2) Δ c e is the most powerful factor in promoting industrial CE, while Δ c i and Δ c l are the factors that inhibit industrial CE in the YRD. (3) η i and η l have promoted the decoupling of industrial CE from economic advancement in the YRD and its three provinces and one city, while η e has consistently been the largest obstacle to the decoupling process. Finally, the policy recommendations proposed in this paper aim to help policy-makers and practitioners in the YRD region achieve a win-win balance between industrial CE and industrial output, and inspire further research on the decoupling and decomposition of industrial CE from an academic perspective.

Exclusionary inclusion? Streaming platforms and trans inclusive policies and practices: A case study of Netflix

This article examines the incongruence within Netflix’s corporate practice: while on the one hand the streamer promotes trans inclusion along with trans talent development in Netflix’s production culture, both on and off screen, on the other hand its industrial practices and representational output demonstrate a different approach. This article argues that, while the streamer positions itself as a champion of inclusion and diversity in the television industry, Netflix is invested in transness as an economic frontier rather than in adopting anti-transphobic practices. In making this argument, this article fosters a three-pronged approach to explore the treatment of trans identities by Netflix. Firstly, the article analyses Netflix’s corporate practices in relation to promotion of its limited series, Tales of the City. Secondly, the article explores the media event of Dave Chappelle’s comedy special The Closer and the subsequent trans employee walkout. Thirdly, it performs on audit of Netflix’s website and corporate materials to highlight how Netflix has developed a response to trans inclusion in relation to the walkout. Exploring these facets through Aron Kundnani’s (2023) critique of the diversity industry, this article argues that, on a policy and corporate performativity level, Netflix’s approach to trans inclusion can be defined as transliberalist (Raha, 2015), so that its practices result in a platform catalogue that is simultaneously at best trans-affirming and at worst transphobic.

The impact of energy (Electricity) losses on load capacity factor: A dynamic ARDL-based evidence from Pakistan

The energy-environment interplay has raised significant concerns among policymakers and experts, with the global shift towards renewable energy being hailed as a key solution to reduce pollution emissions. However, in addition to energy production, the efficient transmission and distribution of electricity is also important which also poses considerable challenges to environmental sustainability. This study aims to investigate the effect of electricity losses on environmental sustainability measured by load capacity factor (LCF). This study uses the DARDL (DARDL) method and Kernel-based Regularized Least Squares (KRLS) to analyze the influence of electricity losses, foreign direct investment (FDI), economic growth, and industrial output on LCF in Pakistan from 1981 to 2022. The electricity losses significantly undermine environmental sustainability by reducing LCF. However, FDI positively influences LCF, but economic growth and industrial output exert a detrimental effect. Additionally, the study observes that both positive and negative counterfactual 10 % shocks in electricity losses result in negative and positive impacts on LCF. Whereas, 10 % positive counterfactual shocks in FDI positively affect LCF. KRLS analysis confirms the robustness of the DARDL estimates. The study underscores that efficient energy transmission and distribution is pivotal in safeguarding environmental sustainability.

Drivers of High Concentration and Dispersal of PM10 and PM2.5 in the Eastern Part of Chhota Nagpur Plateau, India, Investigated Through HYSPLIT Model and Improvement of Environmental Health Quality

ABSTRACTThe ambient air quality is continuously declining especially in emerging countries because of rapid rate of urbanization, industrialization, and allied activities. Thus, the principal objective of this research is to inspect the spatiotemporal variation of particulate matters and to identify the drivers of pollutant dispersal. The month‐wise PM10 and PM2.5 data of 2022 and 2023 were obtained from the West Bengal Pollution Control Board. Meteorological datasets such as wind speed (m/s) and wind direction (degree) were collected from the NASA power access website. The HYSPLIT (Hybrid Single‐Particle Lagrangian Integrated Trajectory) model was applied to identify the pollutant's trajectories. Results showed that Purulia station stood highest in case of absolute AQI 715 (23‐1‐2022 at 7 a.m.), PM2.5 483.47 µg/m3 (23‐1‐2022 at 8 a.m.), and PM10 682.28 µg/m3 (23‐1‐2022 at 7 a.m.), respectively. During winter, the vertical expansion of particles was within the 100‐m boundary layer while during summer, it dispersed vertically and crossed 2000 m above the ground surface due to the combined effect of atmospheric conditions such as wind speed, wind direction, air pressure, and temperature. Moreover, the industrial outputs of Jharkhand and West Bengal play a significant role in elevating the pollution level. Plantation of suitable pollution‐resistant plants including Banyan (Ficus bengalensis), Mango (Mangifera indica), Peepul (Ficus religiosa), Guava (Psidium guajava), Kadam (Neolamarkia cadamba), Neem (Azadirachta indica) is a suitable management technique against pollution in this region.

Analysis of the integrated role of the Yangtze River Delta based on the industrial economic resilience of cities during COVID-19

The enhancement of regional comprehensive development ability is significantly impacted by the study on the implementation effect of regional integration strategies. The integration strategy’s impact on urban development during COVID-19 in the Yangtze River Delta(YRD) is unclear. According to prior industrial transfer theory, Hefei, Anhui’s capital, is difficult to transfer industries, and other YRD cities push industry integration in Anhui. This study employs the theory of economic and land resource use to examine the resilience of the industrial economy during an epidemic by using industrial land as a representation of industrial economic development. The three cities in Anhui—Wuhu, Maanshan, and Chuzhou (Wu-ma-Chu) were selected as the research area. The study employed the UNet deep learning method to detect the land use types in Wu-ma-Chu. The land transfer matrix and the standard deviation ellipse were utilised to research the alterations in industrial land use and the spatial distribution of industrial output value, respectively. The results showed that the industrial land in Machu continued to grow during the outbreak, highlighting the resilience of the region’s industrial economy. During 2019–2022, the elliptical ring of industrial output value is distributed in Nanjing, revealing the radiating role of Nanjing in integrating into the integration of the YRD. This confirms China’s YRD integration strategy, strengthens regional economic resilience, and encourages coordinated regional economic development.

The Impact of Sports Industry Output on Economic Growth: Evidence from China

The Impact of Sports Industry Output on Economic Growth: Evidence from China

The dynamic effects of oil supply shock on China: Evidence from the TVP-Proxy-VAR approach

This study examines how changes in oil supply impact oil prices and China's macroeconomy. We developed an estimator that uses OPEC's institutional characteristics and high-frequency data to identify an oil supply surprise shock. This oil supply surprise is then incorporated as an exogenous variable into a TVP Proxy VAR model to analyze the effects of oil supply shocks on China's economy. Our findings indicate that negative oil supply shocks lead to higher oil prices, which in turn cause increases in China's interest rates and inflation, a decline in stock prices, a short-term rise in exports, and a long-term decrease in industrial output. When considering time-varying model parameters, we found that periods with larger oil supply shocks have a more pronounced impact on the macroeconomy. Our research provides empirical evidence for a better understanding of the effects of energy supply fluctuations.

Analisis Pendapatan dan Kelayakan Usaha Salad Buah pada Mf Dessert

Income is all income received from other parties in the form of money or goods, as well as industrial output, which is valued based on the current amount of money from these assets. Turnover is one of the company's components and the main objective in preparing the income statement. This research aims to conduct an analysis of fruit salad income at Mf Dessert. Through this careful research and comprehensive literature review, it is hoped that we can provide deeper insight into the income that contributes to Fruit Salad at Mf Dessert and provide recommendations that can help Mf Dessert businesses optimize their business potential. The method used is descriptive qualitative. The results of this research show that the average revenue from Mf Dessert is IDR 3,900,000,- per 5 months with production costs incurred of IDR 3,484,873,- per 5 months. The production costs consist of fixed costs of IDR 4,000 per 5 months and variable costs of IDR 3,435,000 per 5 months. From the results of data analysis, it was found that the income earned by fruit salad at Mf Dessert was IDR 415,127 per 5 months. Based on feasibility research on Mf Dessert (R/C Ratio), a value (R/C Ratio) of 1.22 or 1.22 > 1 was obtained, meaning that for every cost incurred Rp. 1, it produces income of Rp. 1.22 so that the revenue is greater than the capital spent. So it can be concluded that the fruit salad at Mf Dessert can be said to be profitable and worth running.

Impacts of Shocks to U.S. Macroeconomic Variables on the Regional Economy of Georgia

This paper develops a way to analyze a regional economy and a large economy within a model. Using structural autoregression with block exogeneity and mutual independence assumptions as well as Cholesky factorization, we quantify the impacts of shocks to U.S. macroeconomic variables on the regional economy of Georgia. We found that the largest impact on Georgia's aggregate variables - consumer prices, real per capita personal income, and total employment - is the federal funds rate. For other shocks, Georgia's personal income is most affected by an increase in commodity prices, while Georgia's consumer prices are most affected by U.S. inflation. At the industry level, we found that the impact of real GDP is the largest and the impact of M2 is the smallest on output and employment in Georgia's industries, although the magnitude of the impact on employment is smaller and more short-lived than on output.

Exchange Rate Depreciation and the Nigeria’s Agricultural and Industrial Sectors

This study examines the effect of exchange rate depreciation on the agricultural and industrial sectors of the Nigerian economy using annual data from 1981 to 2021 in an autoregressive distributed lag (ARDL) model. The findings show that the effect of exchange rate depreciation on industrial and agricultural output was fairly similar. Exchange rate depreciation, inflation, interest rate, and government expenditure have negative effects on industrial output in the short run and positive effects in the long run. Similarly, past agricultural output, exchange rate depreciation, and government expenditure have negative effects on agricultural output in the short run and a positive effect in the long run. Inflation also has a long-term positive effect on agricultural output. Government expenditure had the most substantial long-run effect on industrial and agricultural output. The study shows the need to manage high exchange rate volatility to facilitate realistic forecasts and sound production decisions in the agricultural and industrial sectors. The findings of this study underscore the importance of considering both short-run dynamics and long-run adjustments in understanding the effects of exchange rate depreciation and other economic variables on the industrial and agricultural sectors.

The impact of agricultural value chains on sustainable industrial development in Sub-Saharan Africa

This research identifies the critical challenges surrounding agricultural value chains and their role in fostering sustainable industrial development in Sub-Saharan Africa (SSA) between 1990 and 2022, utilizing the Panel Least Squares (PLS) technique. The paper reports that agricultural value added (p < 0.01), farm machinery (p < 0.05), human capital (p < 0.01), and access to electricity (p < 0.01) exhibited a positive and significant influence on industrial sector output. Conversely, agricultural raw materials exports (p < 0.01) had a negative and significant impact on industrial sector output (p < 0.01). In conclusion, the study underscores the critical role of agricultural value chains in fueling sustainable industrial development in SSA and recommends that policymakers formulate plans to leverage agricultural value chains as catalysts for enduring economic growth and development.